The brain infers our spatial orientation and properties of the world from ambiguous and noisy sensory cues. Judging self-motion (heading) in the presence of independently moving objects poses a challenging inference problem because the image motion of an object could be attributed to movement of the object, self-motion, or some combination of the two. We test whether perception of heading and object motion follows predictions of a normative causal inference framework. In a dual-report task, subjects indicated whether an object appeared stationary or moving in the virtual world, while simultaneously judging their heading. Consistent with causal inference predictions, the proportion of object stationarity reports, as well as the accuracy and precision of heading judgments, depended on the speed of object motion. Critically, biases in perceived heading declined when the object was perceived to be moving in the world. Our findings suggest that the brain interprets object motion and self-motion using a causal inference framework.
Path integration is a strategy by which animals track their position by integrating their self-motion velocity. To identify the computational origins of bias in visual path integration, we asked human subjects to navigate in a virtual environment using optic flow and found that they generally traveled beyond the goal location. Such a behavior could stem from leaky integration of unbiased self-motion velocity estimates or from a prior expectation favoring slower speeds that causes velocity underestimation. Testing both alternatives using a probabilistic framework that maximizes expected reward, we found that subjects' biases were better explained by a slow-speed prior than imperfect integration. When subjects integrate paths over long periods, this framework intriguingly predicts a distance-dependent bias reversal due to buildup of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration in noisy environments is limited largely by biases in processing optic flow rather than by leaky integration.
Previous studies have shown that multiple sessions of reach training lead to long-term improvements in movement time and smoothness in individuals post-stroke. Yet, such long-term training regimens are often difficult to implement in actual clinical settings. Here, we evaluated the long-term and generalization effects of short-duration and intensive reach training in 16 individuals with chronic stroke and mild to moderate impairments. Participants performed two sessions of unassisted intensive reach training, with 600 movements per session, and with display of performance-based feedback after each movement. The participants’ trunks were restrained with a belt to avoid compensatory movements. Training resulted in significant and durable (1-month) improvements in movement time (20.4% on average) and movement smoothness (22.7% on average). The largest improvements occurred in individuals with the largest initial motor impairments. In addition, training induced generalization to non-trained targets, which persisted in 1-day and in 1-month retention tests. Finally, there was a significant improvement in the Box and Block test from baseline to 1-month retention test (23% on average). Thus, short-duration and intensive reach training can lead to generalized and durable benefits in individuals with chronic stroke and mild to moderate impairments.
Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here, we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects’ dynamic belief states. Both groups showed a previously documented bias pattern by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a nonuniform speed prior rather than imperfect integration. We tracked participants’ beliefs and found no difference in the speed prior, but there was heightened variability in the ASD group. Both end point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced, and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.
16Path integration is a navigation strategy by which animals track their position by integrating their self-17 motion velocity over time. To identify the computational origins of bias in visual path integration, we asked 18 human subjects to navigate in a virtual environment using optic flow, and found that they generally travelled 19 beyond the goal location. Such a behaviour could stem from leaky integration of unbiased self-motion 20 velocity estimates, or from a prior expectation favouring slower speeds that causes underestimation of 21 velocity. We tested both alternatives using a probabilistic framework that maximizes expected reward, and 22 found that subjects' biases were better explained by a slow-speed prior than imperfect integration. When 23 subjects integrate paths over long periods, this framework intriguingly predicts a distance-dependent bias 24 reversal due to build-up of uncertainty, which we also confirmed experimentally. These results suggest that 25 visual path integration performance is limited largely by biases in processing optic flow rather than by 26 suboptimal signal integration.
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